Deep Residual Networks have reached the state of the art in many image
processing tasks such image classification. However, the cost for a gain in
accuracy in terms of depth and memory is prohibitive as it requires a higher
number of residual blocks, up to double the initial value. To tackle this
problem, we propose in this paper a way to reduce the redundant information of
the networks. We share the weights of convolutional layers between residual
blocks operating at the same spatial scale. The signal flows multiple times in
the same convolutional layer. The resulting architecture, called ShaResNet,
contains block specific layers and shared layers. These ShaResNet are trained
exactly in the same fashion as the commonly used residual networks. We show, on
the one hand, that they are almost as efficient as their sequential
counterparts while involving less parameters, and on the other hand that they
are more efficient than a residual network with the same number of parameters.
For example, a 152-layer-deep residual network can be reduced to 106
convolutional layers, i.e. a parameter gain of 39\%, while loosing less than
0.2\% accuracy on ImageNet.

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